class_docstr: Simultaneous Translation
  Text agent for Japanese
class_docstr_tok: ["Simultaneous","Translation","Text","agent","for","Japanese"]
code: def predict(self, states):
      # Predict target token from decoder states
      decoder_states = states.decoder_out
  
      lprobs = self.model.get_normalized_probs(
          [decoder_states[:, -1:]], log_probs=True
      )
  
      index = lprobs.argmax(dim=-1)[0, 0].item()
  
      if index != self.dict["tgt"].eos_index:
          token = self.dict["tgt"].string([index])
      else:
          token = self.dict["tgt"].eos_word
  
      return token
  
file_path: fairseq/examples/simultaneous_translation/eval/agents/simul_t2t_enja.py
imports: import os
  from fairseq import checkpoint_utils, tasks
  import sentencepiece
  import torch
label_desc: Use image node_id 12 for calling the SimulTransTextAgentJA obj's underlying member method code with example usage: obj.predict(states) and returns: token